Electric vehicle charging pile reactive power regulation method and system based on cloud processing

Abstract

The invention relates to an electric vehicle charging pile reactive power regulation method and system based on cloud processing. The method comprises the steps that real-time electric quantity and operation state data are collected through an intelligent electric meter and a charging pile; utilizing historical data and a machine learning algorithm to predict a reactive power regulation range meeting voltage safety constraints; based on the target power factor or the power grid adjustment instruction, establishing a reactive power optimization model, and calculating a reactive power distribution scheme of the charging pile; and issuing the optimization instruction to each charging pile for execution, and dynamically adjusting the strategy. The system comprises a data acquisition unit, a communication unit, a cloud processing unit, an instruction issuing unit and a user interface unit, and centralized management and optimization of reactive power regulation of the charging pile are realized. Compared with a traditional mode, the bidirectional reactive power regulation capability of the charging pile is fully utilized, the equipment investment is reduced, the energy efficiency is improved, high efficiency and flexibility are achieved, the requirement of a future power grid for reactive power regulation is met, and the wide application prospect is achieved.

Description

Technical Field

[0001] The present invention belongs to the technical field of charging piles, and in particular relates to a reactive power regulation method and system for an electric vehicle charging pile based on cloud processing. Background Art

[0002] With the widespread promotion and application of distributed photovoltaic power generation and electric vehicle charging piles, the power consumption characteristics on the user side have changed significantly. The access of distributed photovoltaic power generation makes the power supply on the user side highly random and volatile; at the same time, the charging load of electric vehicles has the characteristics of fast instantaneous changes and large power demand. These factors lead to rapid changes in load on the user side and frequent voltage fluctuations, which brings great challenges to the stable operation of power equipment.

[0003] Traditional voltage and reactive power regulation methods mainly rely on transformer tap adjustment and capacitor switching, but these solutions usually face the problems of slow response, bulky size and high cost, and are not suitable for facing the uncertainty of photovoltaic power generation and the rapid change of electric vehicle charging load. At the same time, because the user side is generally only equipped with capacitors, it can only provide reactive power but not absorb reactive power, and cannot regulate overvoltage when photovoltaic power generation is large or the load of the station area is low.

[0004] In addition, when the power supply company issues electricity bills, for power users whose installed capacity exceeds a certain value, the electricity fee will be adjusted according to the average power factor during the billing period, i.e., the power adjustment fee. If the power factor is lower than the assessment standard, a power adjustment fee penalty will be added, otherwise part of the electricity fee will be reduced as a power adjustment fee reward.

[0005] At the same time, as the grid's demand for reactive power regulation increases, power users may gradually be required to participate in the grid's reactive power regulation in the future. By responding to the grid's reactive power regulation needs, power users can obtain regulation benefits.

[0006] Although electric vehicle charging piles have the potential for bidirectional reactive power regulation due to the use of power electronic charging modules, there is currently a lack of effective methods to integrate and utilize these dispersed reactive resources. Current technical deficiencies not only limit the efficiency of grid management, but also fail to fully utilize the potential of electric vehicle charging facilities in reactive power regulation and improving voltage quality. Therefore, there is an urgent need for a new reactive power regulation method that can fully integrate and utilize the reactive power regulation capabilities of electric vehicle charging piles to improve the reactive power management capabilities of power users, which is directly reflected in the user's electricity bill savings and voltage quality improvement. Summary of the invention

[0007] The present invention relates to a reactive power optimization method and system for electric vehicle charging piles based on cloud processing, aiming to improve the power factor of power users, improve voltage quality, and respond to reactive power regulation requirements of power grids.

[0008] A first aspect of the present invention provides a reactive power optimization method for an electric vehicle charging pile based on cloud processing, the method comprising the following steps:

[0009] Data collection step: collecting electrical quantities including power factor, active power, reactive power and voltage in real time through smart meters, and collecting operation status data of each charging pile at the same time, the operation status data including the working status, real-time active power and real-time reactive power of the charging pile, and uploading the electrical quantities and the operation status data to a cloud processing unit for storage to form historical data;

[0010] Reactive power regulation range prediction step: Based on the historical data, a relationship model between active power, reactive power, voltage, voltage difference and reactive power regulation is established through a machine learning algorithm, and the relationship model includes:

[0011] According to the preset voltage upper limit value and voltage lower limit value, calculate the voltage difference between the current voltage and the upper limit value and the lower limit value;

[0012] Inputting the voltage difference, current active power and current reactive power into the relationship model, and predicting the upper and lower limits of reactive power regulation that meet voltage safety constraints;

[0013] Reactive power optimization calculation steps:

[0014] Determine the target reactive power according to the power factor target value set by the user or the reactive power regulation instruction received from the power grid;

[0015] Based on the real-time power and maximum apparent power of each charging pile, calculate the upper and lower limits of reactive power regulation for each charging pile;

[0016] Under the condition of satisfying the reactive power regulation constraints of each charging pile, the reactive power output distribution plan of each charging pile is calculated through the optimization algorithm;

[0017] Instruction execution steps:

[0018] Convert the reactive power distribution plan into reactive power regulation instructions and send them to each charging pile;

[0019] Monitor the performance of charging piles in real time and collect electrical quantities after reactive power regulation;

[0020] According to the monitoring results, it is determined whether the target requirements are met. If not, the method returns to the reactive power optimization calculation step to recalculate the allocation plan.

[0021] Furthermore, the reactive power regulation range prediction uses machine learning algorithms to model historical data to accurately predict the reactive power regulation range that meets voltage safety constraints.

[0022] Furthermore, the objective function is:

[0023]

[0024] Among them, Q target is the total reactive power target value of the charging pile, Q i is the reactive power output of the ith charging pile, and Z is the absolute value of the maximum reactive power regulation of the charging pile.

[0025] The second aspect of the present invention further provides a reactive power regulation system for electric vehicle charging piles based on cloud processing, the system comprising:

[0026] Data acquisition unit: used to collect electrical quantity and charging pile operation status data;

[0027] Communication unit: used to upload data and receive instructions;

[0028] Cloud processing unit: including data storage and preprocessing module, reactive power regulation range prediction module and reactive power optimization module;

[0029] Control instruction sending unit: used to send reactive power adjustment instructions to the charging pile;

[0030] User interface unit: used to visually display the system operation status and configuration parameters.

[0031] Furthermore, the cloud processing unit uses a machine learning algorithm to predict the reactive power regulation range and adopts a mathematical optimization method to calculate the optimal reactive power distribution plan.

[0032] Through the above method and system, the present invention realizes the centralized management and optimized utilization of the reactive power regulation capability of electric vehicle charging piles, improves the reactive power management capability on the power user side, improves the voltage quality, reduces the electricity cost caused by low power factor, and can respond to the reactive power regulation needs of the power grid, and has good application prospects.

[0033] Compared with the prior art, the present invention has the following beneficial effects:

[0034] The present invention uses cloud processing technology to uniformly manage the reactive power regulation capabilities of decentralized charging piles, and effectively utilizes the application value of electric vehicle charging piles in reactive power regulation through machine learning technology and advanced reactive power optimization algorithms. By regulating reactive power, the present invention can significantly improve the power factor of power users, improve voltage quality, and enhance the overall stability of the power grid. Compared with traditional reactive power compensation devices, the present invention fully utilizes the reactive power regulation potential of electric vehicle charging piles themselves, reduces or replaces the investment and construction requirements of conventional reactive power compensation equipment, thereby reducing equipment investment costs.

[0035] In addition, the cloud-based centralized management and optimization functions provided by the present invention make reactive power regulation more flexible and efficient. Users can not only monitor the system operation status in real time, but also flexibly adjust the reactive power regulation strategy according to actual needs to cope with various complex power grid environments. This efficient and intelligent regulation method can not only help power users reduce electricity fines caused by low power factor, but also increase the chances of power users obtaining reactive power regulation rewards, thereby significantly reducing electricity costs.

[0036] In the scenario where multiple charging stations work together, the present invention can coordinate the reactive power output of multiple charging stations through the cloud system to achieve overall optimization of the regional power grid. This not only improves the operating efficiency of the regional power grid, but also provides an effective reactive power regulation method for the power grid, which helps to stabilize the operation of the power grid and reduce the risk of voltage fluctuations.

[0037] As the demand for reactive power regulation increases in the future, the system of the present invention can also connect with the power grid dispatching center, respond to external reactive power regulation instructions, and provide fast and accurate reactive power regulation services for the power grid. This highly automated and intelligent regulation system will play an important role in the future power market, helping power users and power grid operators achieve higher operating benefits.

[0038] In summary, the present invention not only significantly improves the reactive power regulation capability of electric vehicle charging piles, but also reduces equipment investment and operating costs through centralized management and optimization in the cloud, and improves the power utilization efficiency of power users and the overall stability of the power grid. The invention has broad application prospects, and is particularly suitable for various power grid environments that need to optimize the power factor of the power grid and improve energy efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] Figure 1 It is a flow chart of the reactive power regulation method of electric vehicle charging pile based on cloud processing proposed by the present invention;

[0040] Figure 2 It is a system architecture diagram of the reactive power regulation system of electric vehicle charging piles based on cloud processing proposed by the present invention;

[0041] Figure 3 It is a flow chart of embodiment 1. DETAILED DESCRIPTION

[0042] The electric vehicle charging pile reactive power optimization method and system based on cloud processing of the present invention are described in detail below in conjunction with the accompanying drawings and embodiments.

[0043] Embodiment 1: Reactive power optimization method based on cloud

[0044] This embodiment provides a reactive power optimization method for electric vehicle charging piles based on cloud processing, and the specific steps are as follows:

[0045] Data collection

[0046] Install smart meters at the power grid gateway to collect the following electrical quantities in real time:

[0047] Active power P: represents the real-time active power consumption of power users.

[0048] Reactive power Q: represents the real-time reactive power consumption of power users.

[0049] Voltage V: The real-time voltage value of the power gateway.

[0050] Power factor PF: indicates the power factor of the electricity user.

[0051] The following data is obtained from each electric vehicle charging station:

[0052] Operating status: whether the charging station is running, idle or faulty.

[0053] Output power: The current output active power and reactive power of the charging pile.

[0054] Reactive power regulation range prediction

[0055] Using the collected historical data, a relationship model between power grid parameters and reactive power regulation is established. The specific implementation is as follows:

[0056] Data preprocessing: Clean historical data, remove outliers and missing values, and ensure data quality.

[0057] Feature extraction: Select key features, such as active power P, reactive power Q, voltage V, voltage change ΔV (voltage difference from the previous time point), etc.

[0058] Model training: Use machine learning algorithms (such as LightGBM) to train the prediction model and establish the relationship between active power, reactive power, voltage, voltage change and reactive power regulation.

[0059] Real-time prediction: Input the current real-time data and use the trained model to predict the reactive power regulation range under the voltage safety constraint conditions, that is, the upper and lower limits of reactive power Q min and Q max .

[0060] Calculate the voltage difference according to the set voltage upper limits Vmax and Vmin

[0061] ΔV up =V max -V current

[0062] ΔV down =Vcurrent -V min

[0063] The above data is used to form a feature vector and input into the trained machine learning model to predict the reactive power upper and lower adjustment limits ΔQup,limit and ΔQdown,limit that meet the voltage safety constraints in the current state. Then, the upper and lower limits of reactive power are calculated based on the current reactive power.

[0064] Q max =Q current +ΔQ up,limit

[0065] Q min =Q current -ΔQ down,limit

[0066] Reactive power optimization calculation

[0067] According to the predicted reactive power regulation range, reactive power optimization calculation is performed:

[0068] Determine the target reactive power Q target :

[0069] Power factor control mode: according to the set power factor target PF target And the current active power P, calculate the target reactive power:

[0070] Q target =P×tan(arccos(PF target ))

[0071] Grid reactive power regulation mode: If the reactive power regulation command of the grid is received, Q target Set to the command value.

[0072] Range Constraints: Ensure Q target In the predicted reactive power regulation range:

[0073] Q min ≤Q target ≤Q max

[0074] If it exceeds the range, the corresponding upper and lower limits are taken.

[0075] Establish an optimization model to optimize the allocation of reactive power output of each charging pile:

[0076] Objective function: The difference between the total reactive power of the charging pile and the target reactive power is as small as possible. At the same time, in order to prevent the optimization result from being unstable and causing frequent adjustments, the maximum reactive power adjustment absolute value Z is introduced:

[0077]

[0078] Among them, Q target is the total reactive power target value of the charging pile, Q i is the reactive power output of the ith charging pile, and Z is the absolute value of the maximum reactive power regulation:

[0079] Z=max(|ΔQ i |)

[0080] Constraints:

[0081] Charging pile capacity limit:

[0082]

[0083] According to the power circle characteristics of AC / DC:

[0084]

[0085] Q i,min =-Q i,max

[0086] Where Q i,min , Q i,max They are the lower and upper reactive power limits of the charging pile, S i,max is the maximum apparent power of the charging pile, P i It is the real-time power of the charging pile.

[0087] Solution method: The SCIP solver is used to solve the above optimization problem and obtain the optimal reactive power distribution plan for each charging pile.

[0088] Instruction issuance and execution

[0089] Instruction generation: Generate reactive power adjustment instructions based on the optimization calculation results, including the charging pile number and the corresponding reactive power setting value.

[0090] Instruction issuance: Instructions are issued to each charging pile through the communication unit.

[0091] Execution monitoring: monitor the execution status of the charging pile to ensure that the instructions are executed correctly.

[0092] Dynamic Adjustment

[0093] According to the real-time monitoring results and new collected data, the above steps are executed cyclically to dynamically adjust the reactive power regulation strategy to adapt to the changes in the power grid and load.

[0094] Example 2: Cloud-based reactive power regulation system

[0095] This embodiment provides a reactive power regulation system for electric vehicle charging piles based on cloud processing, and its structure is as follows:

[0096] Data acquisition unit

[0097] Smart meter at the power grid gateway: installed at the connection between the power user and the power grid to collect electrical parameters in real time.

[0098] Charging pile data acquisition module: embedded inside the charging pile, collects the operating status and power data of the charging pile.

[0099] Communication unit

[0100] Deployed in the charging pile controller, it uses wired or wireless communication to transmit data and send and receive instructions with the cloud server.

[0101] Cloud Processing Unit

[0102] Data storage and preprocessing module: stores historical data, performs data cleaning and preprocessing.

[0103] Reactive power regulation range prediction module: predicts the reactive power regulation range based on historical data and real-time data.

[0104] Reactive power optimization module: performs reactive power optimization calculations and generates the optimal regulation plan.

[0105] Control command issuing unit

[0106] The reactive power adjustment instructions calculated in the cloud are sent to each charging pile to control its reactive power output.

[0107] User interface unit

[0108] Web visualization interface: allows users to view system operating status, set power factor targets, monitor voltage quality, etc.

[0109] Parameter configuration interface: allows users to configure system parameters, such as power factor target value, voltage safety range, etc.

[0110] Example 3: Responding to the reactive power regulation demand of the power grid

[0111] When there is a reactive power shortage in the power grid, the power grid dispatching center sends a reactive power regulation instruction to the power user. By using the system of the present invention, the power user can respond to the reactive power regulation demand of the power grid. The specific implementation steps are as follows:

[0112] Receiving instructions

[0113] The system receives reactive power regulation instructions issued by the power grid dispatching center through the communication unit, such as increasing reactive power by 100kVar.

[0114] Adjust target value

[0115] Q target Set to the current reactive power plus the regulation required by the grid.

[0116] Reactive power optimization calculation refers to the steps in Example 1 to recalculate the reactive power distribution plan of each charging pile.

[0117] Execution and Feedback

[0118] Send adjustment instructions to the charging pile to adjust the reactive power output.

[0119] The execution results and current reactive power are fed back to the power grid dispatching center.

Claims

1. A reactive power regulation method for electric vehicle charging piles based on cloud processing, characterized in that: The method comprises the following steps: Data collection step: collecting electrical quantities including power factor, active power, reactive power and voltage in real time through smart meters, and collecting operation status data of each charging pile at the same time, the operation status data including the working status, real-time active power and real-time reactive power of the charging pile, and uploading the electrical quantities and the operation status data to a cloud processing unit for storage to form historical data; Reactive power regulation range prediction step: Based on the historical data, a relationship model between active power, reactive power, voltage, voltage difference and reactive power regulation is established through a machine learning algorithm, and the relationship model includes: According to the preset voltage upper limit value and voltage lower limit value, calculate the voltage difference between the current voltage and the upper limit value and the lower limit value; Inputting the voltage difference, current active power and current reactive power into the relationship model, and predicting the upper and lower limits of reactive power regulation that meet voltage safety constraints; Reactive power optimization calculation steps: Determine the target reactive power according to the power factor target value set by the user or the reactive power regulation instruction received from the power grid; Based on the real-time power and maximum apparent power of each charging pile, calculate the upper and lower limits of reactive power regulation for each charging pile; Under the condition of satisfying the reactive power regulation constraints of each charging pile, the reactive power output distribution plan of each charging pile is calculated through the optimization algorithm; Instruction execution steps: Convert the reactive power distribution plan into reactive power regulation instructions and send them to each charging pile; Monitor the performance of charging piles in real time and collect electrical quantities after reactive power regulation; According to the monitoring results, it is determined whether the target requirements are met. If not, the method returns to the reactive power optimization calculation step to recalculate the allocation plan.

2. The reactive power regulation method for electric vehicle charging piles based on cloud processing according to claim 1 is characterized in that: Reactive power regulation range prediction uses machine learning algorithms to model historical data to accurately predict the reactive power regulation range that meets voltage safety constraints.

3. The reactive power regulation method for electric vehicle charging piles based on cloud processing according to claim 1 is characterized in that: The objective function is: Among them, Q target is the total reactive power target value of the charging pile, Q i is the reactive power output of the ith charging pile, and Z is the absolute value of the maximum reactive power regulation of the charging pile.

4. A reactive power regulation system for electric vehicle charging piles based on cloud processing, characterized in that: The system is used to execute the method according to any one of claims 1 to 3, including: Data acquisition unit: used to collect electrical quantity and charging pile operation status data; Communication unit: used to upload data and receive instructions; Cloud processing unit: including data storage and preprocessing module, reactive power regulation range prediction module and reactive power optimization module; Control instruction sending unit: used to send reactive power adjustment instructions to the charging pile; User interface unit: used to visually display the system operation status and configuration parameters.

5. The reactive power regulation system for electric vehicle charging piles based on cloud processing according to claim 4 is characterized in that: The cloud processing unit uses a machine learning algorithm to predict the reactive power regulation range and adopts a mathematical optimization method to calculate the optimal reactive power distribution plan.

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